Falls are the public health issue for the elderly all over the world since
the fall-induced injuries are associated with a large amount of healthcare
cost. Falls can cause serious injuries, even leading to death if the elderly
suffers a "long-lie". Hence, a reliable fall detection (FD) system is required
to provide an emergency alarm for first aid. Due to the advances in wearable
device technology and artificial intelligence, some fall detection systems have
been developed using machine learning and deep learning methods to analyze the
signal collected from accelerometer and gyroscopes. In order to achieve better
fall detection performance, an ensemble model that combines a coarse-fine
convolutional neural network and gated recurrent unit is proposed in this
study. The parallel structure design used in this model restores the different
grains of spatial characteristics and capture temporal dependencies for feature
representation. This study applies the FallAllD public dataset to validate the
reliability of the proposed model, which achieves a recall, precision, and
F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate
the reliability of the proposed ensemble model in discriminating falls from
daily living activities and its superior performance compared to the
state-of-the-art convolutional neural network long short-term memory (CNN-LSTM)
for FD